Sivakorn Sanguanmoo

Job Market Paper

Robustness and Simple Contracts in Sequential Screening

Sellers often contract with buyers before buyers learn their valuations, but observed contracts are usually simpler than the theory of Bayesian sequential screening predicts. To explain this gap, we study a sequential-screening environment in which the seller knows the buyer’s initial willingness to pay and the distribution of his final valuation (demand), but not how the buyer learns. The seller maximizes her worst-case profit over all information structures consistent with what she knows. Under natural conditions on cost and the shape of demand, a single refund contract that the buyer always accepts is essentially the unique robustly optimal mechanism. When these conditions fail, a menu of non-refundable and refundable contracts can dominate all single refund contracts. We quantify how much the seller gains from knowing how the buyer learns and show that this knowledge can also raise total welfare.

Working Papers

Attention Capture (with Andrew Koh)

Revise and Resubmit, Journal of Political Economy

We develop a unified analysis of how information captures attention. A decision maker (DM) faces a dynamic information structure and decides when to stop paying attention. We characterize the convex-order frontier and extreme points of feasible stopping times, as well as dynamic information structures which implement them. This delivers the form of optimal attention capture as a function of the designer and DM’s relative time preferences. Intertemporal commitment is unnecessary: sequentially optimal information structures always exist by inducing stochastic interim beliefs. We further analyze optimal attention capture under noninstrumental value for information. Our results speak directly to the attention economy.

Robust Technology Regulation (with Andrew Koh)

Extended abstract in the 27th ACM conference on Economics and Computation

We analyze how uncertain technologies should be robustly regulated and how regulation should evolve with new information. An adaptive sandbox comprising a zero marginal tax up to an evolving quantity limit is (i) robust: it delivers optimal payoff guarantees when the agent’s learning process and/or preferences are chosen adversarially; (ii) dominant: it outperforms other robust and regular mechanisms across all agent learning processes and preferences; (iii) time-consistent: it is the only robust mechanism that can be implemented without commitment. Robustness is important: absent robust regulation, worst-case payoffs can be arbitrarily poor and are induced by weak but growing optimism that encourages excessive risk-taking. Our results offer optimality foundations for existing policy and speak directly to current debates around managing emerging technologies.

Persuasion and Optimal Stopping (with Andrew Koh and Weijie Zhong) 

Extended abstract in the 27th ACM conference on Economics and Computation

We develop a duality-based first-order approach to dynamic persuasion in optimal stopping problems with general action-, state-, and time-dependent preferences. A direct-communication reduction recasts the design problem as a semi-static program over joint distributions of stopping beliefs and times; strong duality and a near-necessary first-order condition then reduce it to a one-dimensional differential equation in a multiplier that prices the agent's continuation incentive, characterizing the optimum as a concavification of a multiplier-augmented payoff. We demonstrate the method in three applications: dynamic binary persuasion, where optimal policies combine suspense generation with action-targeting; a structural result by which the principal's time-risk preferences alone determine whether suspense is optimal; and dynamic linear persuasion, where the optimum is dynamic tail-censorship.

Informational Puts (with Andrew Koh and Kei Uzui)

Extended abstract in the 25th ACM conference on Economics and Computation

We analyze how dynamic information should be provided to uniquely implement the largest equilibrium in binary-action coordination games. The designer offers an informational put: she stays silent if players choose her preferred action, but injects asymmetric and inconclusive public information if they lose faith. There is (i) no multiplicity gap: the largest (partially) implementable equilibrium can be implemented uniquely; and (ii) no commitment gap: the policy is sequentially optimal. Our results have sharp implications for the design of policy in coordination environments.

Technology Speed Limits (with Andrew Koh)

We study optimal technology regulation when private learning occurs both through doing (scaling up the technology) and through waiting (as time passes). We show that an adaptive speed limit—a cap on the rate at which the technology can increase per unit time—delivers optimal worst-case guarantees over all learning processes and/or preferences, and is the only time-consistent mechanism that does so.

Memory Correlated Equilibrium (with Andrew Koh)

We develop a framework for modeling random and correlated memory in games: a memory correlated equilibrium (MCE) comprises (i) a base extensive-form game; and (ii) a memory structure that delivers self-locating information. We define progressively stronger versions of MCE, show their existence, and develop a revelation principle that paves the way for memory design in games. We illustrate applications to cooperation, mechanisms, coordination, and hold-up problems, as well as discuss implications for the design of algorithms and artificial agents. 

Research in Progress